Description
Facilitates the implementation of processes for machine learning (ML) model productionization to managers. Implements organizational standards around machine learning model readiness for deployment. Promotes organizational strategy around the automation of machine learning workflows. Promotes organizational strategy around trained model/system alignment with design criteria. Implements improvements to organizational processes for the identification and evaluation of potential data quality, security, and/or privacy issues and their impacts on modeling. Facilitates organizational troubleshooting and debugging support processes to address issues in machine learning infrastructure and workflow and create robust solutions. Alleviates the impact of obstacles to cross-functional collaboration efforts with multiple stakeholders to make, adopt and communicate technical decisions and shape the development and delivery of software. Implements organizational processes for the development, refinement, and maintenance of tools, platforms, environments, and services for internal use. Implements improvements to organizational processes for the development of efficient, bug-free code from scratch. Executes organizational strategy to maintain team awareness of current developments in the machine learning field and integration of this knowledge into model development.

Responsibilities

Key
Responsibilities

Machine
Learning and Data Modeling – Model Productionization:

–
Facilitates
the implementation of machine learning (ML) model productionization processes
and process improvements.

–
Uses
technical knowledge and business familiarity to empower the transformation of
machine learning prototypes into production-ready models.

–
Implements
strategy to build technical expertise and readiness across team related to
model productionization.

–
Alleviates
the impact of obstacles on collaboration with multiple stakeholders, such as
Development Leads, Product Management, Operations, and Release Management, to
make, adopt, and communicate technical decisions, and shape the development and
delivery of software.

Model
Development and Deployment – Model Deployment:

–
Implements
multiple team standards around ML model readiness for deployment (e.g., model
scaling, model code cleaning, and meeting production quality standards).

–
Promotes
multiple team strategy around the automation of machine learning workflows,
from data extraction, transformation, and loading (ETL) to model deployment and
monitoring, to establish the continuous integration and continuous delivery of
machine learning solutions.

Model
Development and Deployment – Model Performance:

–
Promotes
multiple team strategies around trained model/system alignment with design
criteria.

–
Identifies
improvements within multiple team processes around deployed model performance




evaluation and troubleshooting.

–
Facilitates
the creation of novel metrics that provide analytical insights to non-technical
stakeholders into how well machine learning models are operating.

Model
Development and Deployment – Data Quality:

–
Implements
improvements to multiple team processes for the identification and evaluation
of potential issues related to data quality (e.g., bias, fairness), data
security, and data privacy, and the minimization of their impacts on data
analyses and modeling.

–
Promotes
multiple team strategies for preparing for and enabling model training.

Internal
Collaborations and Impacts – Model Integration and Operation:

–
Implements
improvements to multiple team strategy that forms partnerships for
collaboration with multiple stakeholders (e.g., data scientists, software
developers) to integrate ML models into new or existing systems.

–
Maintains
accountability of model development and operations teams in the smooth
deployment and continuous improvement of ML models.

–
Builds
the team's knowledge of operational considerations of model deployment (e.g.,
performance, scalability, stability, maintenance) to facilitate multiple team
processes.

–
Facilitates
expert troubleshooting and debugging support efforts to address issues in
machine learning infrastructure and workflow and create robust solutions to
prevent future problems.

Internal
Collaborations and Impacts – Tool Development:

–
Implements
process improvements for the development, refinement, and maintenance of tools,
platforms, environments, and services for internal use.

Internal
Collaborations and Impacts – Coding and Documentation:

–
Implements
improvements to multiple team processes for the development of efficient,
bug-free code from scratch, as well as the maintenance and organization of the
existing codebase.

–
Maintains
team adherence to best practices for version control, code review, and code
delivery/deployment.

–
Monitors
skilled documentation for technical processes (experimentation, data
collection and analyses, model building).

Machine
Learning Expertise:

–
Implements
multiple team strategies to maintain team awareness of current developments in
the machine learning field and integration of this knowledge into model
development.

–
Utilizes
familiarity with the usage and development of third-party machine learning
frameworks, packages,



and libraries (e.g., PyTorch, TensorFlow, Keras) to
identify improvements to multiple team processes around their performance and
scalability, and integrate them into production environments.

Core
Responsibilities

Planning
& Execution:

–
Manages
multiple medium- to large-scale projects or initiatives across teams, ensuring
timelines, deliverables, and budgets when applicable are monitored and met.

–
Provides
direction to teams on project work, setting priorities, and aligning with
business needs.

–
Guides
teams on adjusting plans to accommodate resource or timeline changes.

Collaboration
& Partnership:

–
Drives
cross-functional partnerships to align expectations and shared objectives
across multiple teams.

–
Coaches
team members to develop strategic relationships with business leaders,
stakeholders, and external partners to foster collaboration and long-term
success.

–
Promotes
inclusivity by actively seeking and listening to diverse perspectives, ensuring
others feel heard and respected.

Problem
Solving:

–
Provides
direction to multiple teams on addressing complex operational and/or technical
issues as well as providing guidance on analyzing complex data and/or
information to identify solutions.

–
Reviews
and provides insights into unresolved or critical issues, helping the team to
identify potential solutions.

Continuous
Learning:

–
Models
engaging in continuous learning to deepen expertise and stay ahead of industry
trends, integrating best practices into strategic planning.

–
Leverages
feedback to drive personal and team skill improvements.

–
Identifies
skill gaps across teams, and empowers team members to pursue learning and
knowledge sharing opportunities that build their expertise in new areas and
coaches them to apply learnings to advance the organization.

Continuous
Improvement:

–
Drives
team to collaborate on, develop, and implement ideas to increase the efficiency
and effectiveness of processes, protocols, and workflows within and across
teams, providing oversight.

–
Guides
team to adopt new ideas for alternative approaches and methods and encourages
feedback for continued improvement.

Performance
and Development:

–
Drives
performance across teams by providing feedback and coaching in alignment with
performance management processes, guidelines, and expectations.

–
Discusses
development goals with team members, shares opportunities to facilitate career
development, and ensures individual goals are aligned with broader
organizational goals.

–
Develops
and manages talent acquisition pipeline by leading candidate interviews,
monitoring promotion eligibility, and/or orchestrating talent resources.

Qualifications

Career Level - M3

📌 Senior Manager, Machine Learning Engineering (Bengaluru)
🏢 Important Group
📍 Bengaluru